Agent-ValueBench is the first dedicated benchmark for agent values, showing they diverge from LLM values, form a homogeneous 'Value Tide' across models, and bend under harnesses and skill steering.
Can machines learn morality? the delphi experiment
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Frontier LLMs approximate human story morals but show markedly less cross-linguistic variation and narrower value focus than human responses across 14 language-culture pairs.
LLMs trained on simple specification gaming generalize to zero-shot reward tampering including rewriting their own reward function.
ERTS encodes ethical dilemmas in a 22D space, applies 17 semantic perturbations under 6 constraints, and uses a 4-component index to test 6 models on 1500 cases, finding only 33% pass clearance.
Frontier LLMs exhibit moral deliberative sycophancy by shifting their moral reasoning and justifications up to 6.5% on average toward a user's stated preferred view in simulated deliberations.
LLMs deviate from human moral preferences in kidney allocation scenarios and rarely express indecision, though low-rank fine-tuning with few examples can improve both consistency and uncertainty calibration.
Ranked preference modeling outperforms imitation learning for language model alignment and scales more favorably with model size.
Reanalyzing MoReBench by assigning LLMs the task of generating scoring rubrics shows better calibration to human rubrics and suggests stronger LLM moral reasoning than previously reported.
citing papers explorer
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Agent-ValueBench: A Comprehensive Benchmark for Evaluating Agent Values
Agent-ValueBench is the first dedicated benchmark for agent values, showing they diverge from LLM values, form a homogeneous 'Value Tide' across models, and bend under harnesses and skill steering.
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Lessons Without Borders? Evaluating Cultural Alignment of LLMs Using Multilingual Story Moral Generation
Frontier LLMs approximate human story morals but show markedly less cross-linguistic variation and narrower value focus than human responses across 14 language-culture pairs.
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ERTS: Adversarial Robustness Testing of Ethical AI via Semantic Perturbation in a Bounded Consequence Space
ERTS encodes ethical dilemmas in a 22D space, applies 17 semantic perturbations under 6 constraints, and uses a 4-component index to test 6 models on 1500 cases, finding only 33% pass clearance.
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Normative Robustness as a Frontier for Non-Verifiable Reasoning in LLMs
Frontier LLMs exhibit moral deliberative sycophancy by shifting their moral reasoning and justifications up to 6.5% on average toward a user's stated preferred view in simulated deliberations.
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Are LLMs Bad at Moral Reasoning?
Reanalyzing MoReBench by assigning LLMs the task of generating scoring rubrics shows better calibration to human rubrics and suggests stronger LLM moral reasoning than previously reported.